Waste production classification and analysis: a PCA-induced methodology

Abstract

Knowledge of waste composition and production is a requirement to build an efficient waste management scenario. Analysis of this data at a detailed level of observation (regional or communal) is useful to create adapted local scenarios, thus optimizing the overall waste management. However, working at a detailed level of observation multiplies the number of scenarios to build. In this article, we use Principal Components Analysis (PCA) to identify similarities between local administrative areas. By grouping administrative areas based on their waste production, this analysis is an efficient way to reduce the number of local waste management scenarios to define and it also favors cooperation among similar administrative areas. To illustrate our methodology, we focus on the specific case of Reunion Island, which is composed of 24 municipalities. The PCA analysis enabled to identify 5 groups of municipalities, thus reducing the number of required scenarios to build drastically.